Saama-POI Webinar Slides FINAL 04.27.2016 dm
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Transcript of Saama-POI Webinar Slides FINAL 04.27.2016 dm
1Copyright © 2016, Saama Technologies | Confidential
Data Can Be Simple… Right?
Capture
Ingest
Extract
Aggregate
Cleanse
Visualize
Automate
Migrate
Strategy Repository
Quality Asse
ssmentGovernance
Archaeology
Corruption
Quality Assurance
Harvest Harmonize
Storage
Quality AssuranceLake
Process
Map
Security
AuditMachine Learning
Optimize
Manage
Virtualize
WarehouseRecovery
Proliferation
Science
2Copyright © 2016, Saama Technologies | Confidential
CPG data sources – Wealth of Potential … & Challenges
Traditional Data Sources• Syndicated• POS• Shipments• Spending
Emerging• Crowd-sourced
o Panelo Retail Conditions
• Digital Promo Test• Social Listening• Many Others
Re-purposed Data Sources• Panel• COGs• Weather
3Copyright © 2016, Saama Technologies | Confidential
Data Stages
Acquisition Integration Storage Analytics Decisions
INTEGRATION ALIGNMENT ANALYSIS
VisualizationSecurity and Access Control
Complete Load Autom
ation
DataMart
BI Cube
External Data SourcesInternal Data Sources
POS Data Sell-Thru, Sell-Out
Syndicated
Social & sentiment Data
ERP SAP; Shipment, Pricing,
Sell-in
TPMS
EDW/BWMaster Data, Hierarchy,
Price brackets
PEA DAL
Security and Access Control
Automatic Mapping Engine
Data Harm
onization
LoadValidateCleanse
Transform
APIs
Saama Fluid Analytics Cloud Data Integration Engine
Configurable Confidence %
Thresholds
Rules Engine
Configurable Business Rules
Audit & Error Handling
Run ETL Jobs
Accelerators
PEA Admin WorkbenchCustomer & Product Mapping (override
auto mapping)
Input Data Adjustment Event Dates,
Shipped Volume,
Price & COGS
Updates to PEA Merged:
Outlier Removal, Reported/ Non-
Reported , Shopper
Marketing Spend
Mapped & Un-Mapped Products, Customers
Missing and Misplaced Events
Harmonizer Merging Exceptions
Cleansing and Business Exceptions
Admin Functions
Manual Override
Ad-hocSelf Service BI
PEACanned Reports,
Executive & Analyst
Dashboards, and
Foundation for
Predictive & Prescriptive
analytics
Data Feed
Data Architecture
5Copyright © 2016, Saama Technologies | Confidential
Data Storage and Access
Lost at Sea or Calm in a “Data Lake”?
6Copyright © 2016, Saama Technologies | Confidential
Descriptive Analytics• Effectiveness and efficiency of promotional events• Effectiveness and efficiency of EDLP spend• Drill-down based on customer, product and event hierarchiesDiagnostic Analytics• Under performing and over performing customers, products, deal structures,
promotional tactics, times of year etc. • Link between Pricing and Promotional Strategy• Financial Driver AnalysisPredictive Analytics / Test and Learn• Structured variety of Data• Different price levels, confounding factors• What-if Analysis based on predictive Models Advanced Analytics• Cannibalization of sales of other products vs. truly incremental sales• Retailer forward buy / Pantry Loading• The right baselines (“What would have been”, “business as usual forecast”,
etc.)
Analytical Methodology
7Copyright © 2016, Saama Technologies | Confidential
Drive Strategic Agreement on Business Objective(s)
Incremental Revenue/Turnov
er
Incremental Profit / ROI
Volume / % Lift
Market Share
Series 1 Series 2 Series 3
8Copyright © 2016, Saama Technologies | Confidential
How will each use the system, and maintain consistency of interpretation?
Joint Business Planning – which data to share with retailer
Unified planning process
Field awareness/adoption/incentive to provide accurate data
Study and act upon results, provide diagnostic interpretations
Stakeholder Management / Roles
Account Manage
rs
Brand Manage
rs
Category
Directors
VP
FinanceAnalyst
9Copyright © 2016, Saama Technologies | Confidential
Change Promotional Tactics
Shift spend among Products, Categories & Brands
Reduce / Eliminate unprofitable Spend
Increase Retailer Alignment
Quarterly / Annual Planning Process
Decisions Supported
Shift spend among Retailers
Identify & Expand best Practices
Quarterly / Annual Planning Budgets
10Copyright © 2016, Saama Technologies | Confidential
New POI Whitepaper
11Copyright © 2016, Saama Technologies | Confidential
Inability to Effectively Manage Promotions, and Benefit from them, Stems from Four Key Factors
1. Complexity – Amount of resources/time required to analyze volume of trade promotions,
given current systems, is unsustainable 2. Fidelity:
– The fidelity of financial metrics within trade promotion analytics are highly suspect; end users trust output
3. Data utilization: – Much of the data that might help better inform trade analytics does not end up
being used for analytics due to the difficulty in collecting, normalizing, and analyzing it
4. Data overload: – Increasingly more data is being collected each day, but most of it is not being
utilized. – If anything, it tends to further cloak the problem because of the lack of
resources and inability to get to the data that is most relevant.
12Copyright © 2016, Saama Technologies | Confidential
4 Key Capabilities Required for CPG Data & Analytics Excellence
1) Pre-built Analytics2) Utilizing Advanced Modeling and Data Science3) Merging Disparate Data4) Expertise for Data Enrichment and Cleansing
13Copyright © 2016, Saama Technologies | Confidential
About Saama
13
• Data & advanced analytics solutions company since 1997
• Multi-vertical solutions – High Tech, Insurance, Life Science/Pharma, CPG
• Data scientists, “Big Data” engineers, consultants drive advanced analytics with business insights … Transitioned from Services to Unique, Hybrid Solution
• Global – offices in San Jose, Phoenix, Columbus, London, Basel, & Pune